Table of Contents

2. Linear Models for Continuous Data

2.1. Introduction to Linear Models
2.2. Estimation of the Parameters
2.3. Tests of Hypotheses
2.4. Simple Linear Regression
2.5. Multiple Linear Regression
2.6. One-Way Analysis of Variance
2.7. Two-Way Analysis of Variance
2.8. Analysis of Covariance Models
2.9. Regression Diagnostics
2.10. Transforming the Data

3. Logit Models for Binary Data

3.1. Introduction to Logistic Regression
3.2. Estimation and Hypothesis Testing
3.3. The Comparison of Two Groups
3.4. The Comparison of Several Groups
3.5. Models With Two Predictors
3.6. Multi-factor Models: Model Selection
3.7. Other Choices of Link
3.8. Regression Diagnostics for Binary Data

4. Poisson Models for Count Data

4.1. Introduction to Poisson Regression
4.2. Estimation and Testing
4.3. A Model for Heteroscedastic Counts

5. Log-Linear Models for Contingency Tables

5.1. Models for Two-dimensional Tables
5.2. Models for Three-Dimensional Tables

6. Multinomial Response Models

6.1. The Nature of Multinomial Data
6.2. The Multinomial Logit Model
6.3. The Conditional Logit Model
6.4. The Hierarchical Logit Model
6.5. Models for Ordinal Response Data

7. Survival Models

7.1. The Hazard and Survival Functions
7.2. Censoring and The Likelihood Function
7.3. Approaches to Survival Modeling
7.4. The Piece-Wise Exponential Model
7.5. Infant and Child Mortality in Colombia
7.6. Discrete Time Models

A. Review of Likelihood Theory

A.1. Maximum Likelihood Estimation
A.2. Tests of Hypotheses

B. Generalized Linear Model Theory

B.1. The Model
B.2. Maximum Likelihood Estimation
B.3. Tests of Hypotheses
B.4. Binomial Errors and Link Logit
B.5. Poisson Errors and Link Log